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Studies showed that Brazilian Amazon indigenous territories (ITs) are efficient models for preserving forests by reducing deforestation, fires, and related carbon emissions. Considering the importance of ITs for conserving socio-environmental and cultural diversity and the recent climb in the Brazilian Amazon deforestation, we used official remote sensing datasets to analyze deforestation inside and outside indigenous territories within Brazil's Amazon biome during the 2013–2021 period. Deforestation has increased by 129% inside ITs since 2013, followed by an increase in illegal mining areas. In 2019–2021, deforestation was 195% higher and 30% farther from the borders towards the interior of indigenous territories than in previous years (2013–2018). Furthermore, about 59% of carbon dioxide (CO2) emissions within ITs in 2013–2021 (96 million tons) occurred in the last three years of analyzed years, revealing the magnitude of increasing deforestation to climate impacts. Therefore, curbing deforestation in indigenous territories must be a priority for the Brazilian government to secure these peoples' land rights, ensure the forests' protection and regulate the global climate. [ABSTRACT FROM AUTHOR]

Aim: The aim was to evaluate fire activity for the entire Amazon and Amazon regions within each country/department from 2003 to 2020, assessing the potential contributions of drought and deforestation and contrasting 2020 with the previous years. Location: Amazonia sensu lato. Time period: Annually from 2003 to 2020. Major taxa studied: Terrestrial plants. Methods: We collected time series of MODIS active fire detections and burned area and assessed the yearly burned area of several land‐use/land‐cover types. We also divided the Amazon territory into 10 km × 10 km grid cells to identify annual anomalies in active fire occurrence, rainfall, maximum cumulative water deficit (MCWD) and deforestation. Rainfall and MCWD anomalies for a given cell each year consisted of annual values at least one standard deviation below average for that cell from 2003 to 2020, whereas fire and deforestation anomalies had annual values at least one standard deviation above the average for that cell. Results: Brazil and Bolivia have contributed, on average, >70% and about 15%, respectively, of annual active fire detections in the Amazon, in addition to more than half and about one‐third of annual burned areas in the region. On average, 32% of annual burned areas in the Amazon have consisted of agricultural lands, 29% of natural grasslands and 16% of old‐growth forests. The annual extent of areas with fire anomalies was significantly associated with the annual extent of areas with deforestation anomalies, but not significantly associated with the annual extent of areas experiencing water deficit anomalies. In 2020, the total burned area in the Amazon was the greatest since 2010, and the ratio of burned area per active fire was the second greatest of the time series, despite a much lower extent of areas with anomalously high water deficit in comparison to the 2015–2016 megadrought. Main conclusions Our findings suggest that the majority of anomalously high fire occurrences in the Amazon since 2003 did not occur in anomalous drought conditions. The intensification of agricultural fires and deforestation aggravated the burning of Amazonian ecosystems in 2020. [ABSTRACT FROM AUTHOR]

Tropical forests provide essential environmental services to human well-being. In the world, Brazil has the largest continuous area of these forests. However, in the state of Maranhão, in the eastern Amazon, only 24% of the original forest cover remains. We integrated and analyzed active fires, burned area, land use and land cover, rainfall, and surface temperature datasets to understand forest fragmentation and forest fire dynamics from a remote sensing approach. We found that forest cover in the Maranhão Amazon region had a net reduction of 31,302 km2 between 1985 and 2017, with 63% of losses occurring in forest core areas. Forest edges extent was reduced by 38%, while the size of isolated forest patches increased by 239%. Forest fires impacted, on average, around 1031 ± 695 km2 year−1 of forest edges between 2003 and 2017, the equivalent of 60% of the total burned forest in this period. Our results demonstrated that forest fragmentation is an important factor controlling temporal and spatial variability of forest fires in the eastern Amazon region. Thus, both directly and indirectly, forest fragmentation can compromise biodiversity and carbon stocks in this Amazon region. [ABSTRACT FROM AUTHOR]

The article offers information on Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. It mentions the magnitude of carbon losses at these forest edges is still poorly quantified at large scales due to the scarcity of quantitative datasets for tropical forests, Efforts to accurately incorporate this source to regional and global carbon budgets.

Monitoring changes in tree cover for assessment of deforestation is a premise for policies to reduce carbon emission in the tropics. Here, a U-net deep learning model was used to map monthly tropical tree cover in the Brazilian state of Mato Grosso between 2015 and 2021 using 5 m spatial resolution Planet NICFI satellite images. The accuracy of the tree cover model was extremely high, with an F1-score >0.98, further confirmed by an independent LiDAR validation showing that 95% of tree cover pixels had a height >5 m while 98% of non-tree cover pixels had a height <5 m. The biannual map of deforestation was then built from the monthly tree cover map. The deforestation map showed relatively consistent agreement with the official deforestation map from Brazil (67.2%) but deviated significantly from Global Forest Change (GFC)'s year of forest loss, showing that our product is closest to the product made by visual interpretation. Finally, we estimated that 14.8% of Mato Grosso's total area had undergone clear-cut logging between 2015 and 2021, and that deforestation was increasing, with December 2021, the last date, being the highest. High-resolution imagery from Planet NICFI in conjunction with deep learning techniques can significantly improve the mapping of deforestation extent in tropical regions. [ABSTRACT FROM AUTHOR]

Full-waveform LiDAR (FWF) offers a promising advantage over other technologies to represent the vertical canopy structure of secondary successions in the Amazon region, as the waveform encapsulates the properties of all elements intercepting the emitted beam. In this study, we investigated modifications in the vertical structure of the Amazonian secondary successions across the vegetation gradient from early to advanced stages of vegetation regrowth. The analysis was performed over two distinct climatic regions (Drier and Wetter), designated using the Maximum Cumulative Water Deficit (MCWD). The study area was covered by 309 sample plots distributed along 25 LiDAR transects. The plots were grouped into three successional stages (early—SS1; intermediate—SS2; advanced—SS3). Mature Forest (MF) was used as a reference of comparison. A total of 14 FWF LiDAR metrics from four categories of analysis (Height, Peaks, Understory and Gaussian Decomposition) were extracted using the Waveform LiDAR for Forestry eXtraction (WoLFeX) software (v1.1.1). In addition to examining the variation in these metrics across different successional stages, we calculated their Relative Recovery (RR) with vegetation regrowth, and evaluated their ability to discriminate successional stages using Random Forest (RF). The results showed significant differences in FWF metrics across the successional stages, and within and between sample plots and regions. The Drier region generally exhibited more pronounced differences between successional stages and lower FWF metric values compared to the Wetter region, mainly in the category of height, peaks, and Gaussian decomposition. Furthermore, the Drier region displayed a lower relative recovery of metrics in the early years of succession, compared to the areas of MF, eventually reaching rates akin to those of the Wetter region as succession progressed. Canopy height metrics such as Waveform distance (WD), and Gaussian Decomposition metrics such as Bottom of canopy (BC), Bottom of canopy distance (BCD) and Canopy distance (CD), related to the height of the lower forest stratum, were the most important attributes in discriminating successional stages in both analyzed regions. However, the Drier region exhibited superior discrimination between successional stages, achieving a weighted F1-score of 0.80 compared to 0.73 in the Wetter region. When comparing the metrics from SS in different stages to MF, our findings underscore that secondary forests achieve substantial relative recovery of FWF metrics within the initial 10 years after land abandonment. Regions with potentially slower relative recovery (e.g., Drier regions) may require longer-term planning to ensure success in providing full potential ecosystem services in the Amazon. [ABSTRACT FROM AUTHOR]

The authors concluded that the most reliable vegetation indices for monitoring post-fire vegetation recovery were the Leaf Chlorophyll Content Index (LCCI) and Normalized Difference Red-Edge and SWIR2 (NDRESWIR). The complexity of vegetation fires in South America is observed from two recent fire crises: the 2019 Amazonia fire crisis, which was mainly associated with deforestation [[1]], and the 2020 Pantanal fire crisis [[2]], which was triggered by unprecedented drought and heat wave events [[3], [5]]. Vegetation fires represent a major disturbance in the tropics, with South America notable for having both fire-sensitive (e.g., Amazonia and Atlantic Forest) and fire-prone (e.g., Cerrado and Pantanal) biomes. In a similar study also conducted in southern Ecuador, Maxwald et al. [[9]] identified the most reliable vegetation indices for post-fire vegetation monitoring, and analyzed vegetation recovery across different classes of fire severity. [Extracted from the article]

In the Amazon, deforestation and climate change lead to increased vulnerability to forest degradation, threatening its existing carbon stocks and its capacity as a carbon sink. We use satellite L-Band Vegetation Optical Depth (L-VOD) data that provide an integrated (top-down) estimate of biomass carbon to track changes over 2011–2019. Because the spatial resolution of L-VOD is coarse (0.25°), it allows limited attribution of the observed changes. We therefore combined high-resolution annual maps of forest cover and disturbances with biomass maps to model carbon losses (bottom- up) from deforestation and degradation, and gains from regrowing secondary forests. We show an increase of deforestation and associated degradation losses since 2012 which greatly outweigh secondary forest gains. Degradation accounted for 40% of gross losses. After an increase in 2011, old-growth forests show a net loss of above-ground carbon between 2012 and 2019. The sum of component carbon fluxes in our model is consistent with the total biomass change from L-VOD of 1.3 Pg C over 2012-2019. Across nine Amazon countries, we found that while Brazil contains the majority of biomass stocks (64%), its losses from disturbances were disproportionately high (79% of gross losses). Our multi-source analysis provides a pessimistic assessment of the Amazon carbon balance and highlights the urgent need to stop the recent rise of deforestation and degradation, particularly in the Brazilian Amazon. [ABSTRACT FROM AUTHOR]

While Brazil publicly committed to reduce deforestation in Amazonia at the 26th Conference of the Parties (COP26), the Brazilian parliament is moving toward weakening environmental laws. Deforestation rates continue ascending, reaching in 2021 the highest value since 2006 (13,235 km2). To overcome this paradox, strategies to curb deforestation are mandatory. The current strategy, "Plano Amazônia 21/22," prioritizes law enforcement actions to curb illegal deforestation in only 11 Amazonian municipalities. Here, we show that this prioritization is likely to be insufficient since these municipalities account for just 37% of the current deforestation rate. This strategy may also be undermined by the leakage of deforestation actions to unmonitored municipalities. Using a set of spatially explicit datasets integrated into a deforestation‐prediction modeling approach, we propose a science‐based alternative method for ranking deforestation hotspots to be prioritized by law enforcement actions. Our prioritization method accounts for more than 60% of the deforestation, detecting larger deforested areas in both private and public lands, while covering 27% less territory than "Plano Amazônia 21/22." Optimizing the detection of priority areas for curbing deforestation, as proposed here, is the first step to reducing deforestation rates and comply with the Brazilian legal commitment of 3925 km2 year−1. [ABSTRACT FROM AUTHOR]

MATOPIBA is an agricultural frontier, where fires are essential for its biodiversity maintenance. However, the increase in its recurrence and intensity, as well as accidental fires can lead to socioeconomic and environmental losses. Due to this dual relationship with fire, near real-time (NRT) fire management is required throughout the region. In this context, we developed, to the best of our knowledge, the first Machine Learning (ML) algorithm based on the GOES-16 ABI sensor able to detect and monitor Active Fires (AF) in NRT in MATOPIBA. To do so, we analyzed the best combination of three ML algorithms and how long it takes to consider a historical time series able to support accurate AF predictions. We used the most accurate combination for the final model (FM) development. The results show that the FM ensures an overall accuracy rate of approximately 80%. The FM potential is remarkable not only for single detections but also for a consecutive sequence of positive predictions. Roughly, the FM achieves an accuracy rate peak after around 20 h of consecutive AF detections, but there is an important trade-off between the accuracy and the time required to assemble more fire indications, which can be decisive for firefighters in real life. [ABSTRACT FROM AUTHOR]

The article highlights the various factors contributing to the degradation of Amazonian forests, including edge effects, timber extraction, fire, and droughts. It mentions the need for curbing deforestation and global efforts to reduce greenhouse gas emissions to tackle the problem of degradation, and engaging with the actors promoting degradation, effective monitoring of different disturbances, and refining policy frameworks.